J Shanghai Jiaotong Univ Sci ›› 2024, Vol. 29 ›› Issue (4): 601-612.doi: 10.1007/s12204-024-2732-1
Special Issue: 智能机器人
• Special Issue on Multi-Agent Collaborative Perception and Control • Next Articles
LI Shuyi (李舒逸), LI Minzhe (李旻哲), JING Zhongliang∗ (敬忠良)
Accepted:
2023-10-12
Online:
2024-07-14
Published:
2024-07-14
CLC Number:
LI Shuyi (李舒逸), LI Minzhe (李旻哲), JING Zhongliang∗ (敬忠良). Multi-Agent Path Planning Method Based on Improved Deep Q-Network in Dynamic Environments[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 601-612.
[1] ARADI S. Survey of deep reinforcement learning for motion planning of autonomous vehicles [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(2): 740-759. [2] ZHOU W H, LIU Z H, LI J, et al. Multi-target tracking for unmanned aerial vehicle swarms using deep reinforcement learning [J]. Neurocomputing, 2021, 466: 285-297. [3] HAN R H, CHEN S D, HAO Q. Cooperative multirobot navigation in dynamic environment with deep reinforcement learning [C]//2020 IEEE International Conference on Robotics and Automation. Paris: IEEE, 2020: 448-454. [4] S′ANCHEZ-IB′A?NEZ J R, P′EREZ-DEL-PULGAR C J, GARC′IA-CEREZO A. Path planning for autonomous mobile robots: A review [J]. Sensors, 2021, 21(23): 7898. [5] CHAE S W, SEO Y W, LEE K C. Task difficulty and team diversity on team creativity: Multi-agent simulation approach [J]. Computers in Human Behavior, 2015, 42: 83-92. [6] MA H. Graph-based multi-robot path finding and planning [J]. Current Robotics Reports, 2022, 3(3): 77-84. [7] POUDEL S, ARAFAT M Y, MOH S. Bio-inspired optimization-based path planning algorithms in unmanned aerial vehicles: A survey [J]. Sensors, 2023, 23(6): 3051. [8] HUANG J, JI Z H, XIAO S, et al. Multi-agent vehicle formation control based on mpc and particle swarm optimization algorithm [C]//2022 IEEE 6th Information Technology and Mechatronics Engineering Conference. Chongqing: IEEE, 2022: 288-292. [9] GAO J L, YE W J, GUO J, et al. Deep reinforcement learning for indoor mobile robot path planning [J]. Sensors, 2020, 20(19): 5493. [10] PATLE B K, BABU L G, PANDEY A, et al. A review: On path planning strategies for navigation of mobile robot [J]. Defence Technology, 2019, 15(4): 582-606. [11] SALAMAT B, TONELLO A M. A modelling approach to generate representative UAV trajectories using PSO [C]//2019 27th European Signal Processing Conference. A Coruna: IEEE, 2019: 1-5. [12] BATTOCLETTI G, URBAN R, GODIO S, et al. RLbased path planning for autonomous aerial vehicles in unknown environments [C]//AIAA AVIATION 2021 FORUM. Online: AIAA, 2021: 3016. [13] ZHU K, ZHANG T. Deep reinforcement learning based mobile robot navigation: A review [J]. Tsinghua Science and Technology, 2021, 26(5): 674-691. [14] GARAFFA L C, BASSO M, KONZEN A A, et al. Reinforcement learning for mobile robotics exploration: A survey [J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(8): 3796-3810. [15] LIU F, CHEN C, LI Z H, et al. Research on path planning of robot based on deep reinforcement learning [C]//2020 39th Chinese Control Conference. Shenyang: IEEE, 2020: 3730-3734. [16] YAN C, XIANG X J, WANG C. Towards real-time path planning through deep reinforcement learning for a UAV in dynamic environments [J]. Journal of Intelligent & Robotic Systems, 2020, 98(2): 297-309. [17] RUAN X G, LIN C L, HUANG J, et al. Obstacle avoidance navigation method for robot based on deep reinforcement learning [C]//2022 IEEE 6th Information Technology and Mechatronics Engineering Conference. Chongqing: IEEE, 2022: 1633-1637. [18] HU Z W, CONG S C, SONG T K, et al. AirScope: Mobile robots-assisted cooperative indoor air quality sensing by distributed deep reinforcement learning [J].IEEE Internet of Things Journal, 2020, 7(9): 9189-9200. [19] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing Atari with deep reinforcement learning [DB/OL]. (2013-12-19). http://arxiv.org/abs/1312.5602 [20] VAN HASSELT H, GUEZ A, SILVER D. Deep reinforcement learning with double Q-Learning [C]//Thirtieth AAAI Conference on Artificial Intelligence. Phoenix: ACM, 2016: 2094-2100. [21] SEWAK M. Deep Q Network (DQN), Double DQN, and Dueling DQN: A step towards general artificial intelligence [M]//Deep reinforcement learning: Frontiers of artificial intelligence. Singapore: Springer, 2019: 95-108. [22] PENG B Y, SUN Q, LI S E, et al. End-to-end autonomous driving through dueling double deep Qnetwork [J]. Automotive Innovation, 2021, 4(3): 328-337. [23] SCHAUL T, QUAN J, ANTONOGLOU I, et al. Prioritized experience replay [DB/OL]. (2015-11-18). http://arxiv.org/abs/1511.05952 [24] CHAUHAN R, GHANSHALA K K, JOSHI R C. Convolutional neural network (CNN) for image detection and recognition [C]//2018 First International Conference on Secure Cyber Computing and Communication. Jalandhar: IEEE, 2018: 278-282. [25] MEGALINGAM R K, R A, HEMATEJAANIRUDHBABU D, et al. Implementation of a Person Following Robot in ROS-gazebo platform [C]//2022 International Conference for Advancement in Technology. Goa: IEEE, 2022: 1-5. |
[1] | ZHAO Yanfei1,2,3(赵艳飞), XIAO Peng4 (肖鹏), WANG Jingchuan1,2,3* (王景川), GUO Rui4*(郭锐). Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 27-33. |
[2] | ZHAO Yingce(赵英策), ZHANG Guanghao(张广浩), XING Zhengyu(邢正宇), LI Jianxun(李建勋). Hierarchical Reinforcement Learning Adversarial Algorithm Against Opponent with Fixed Offensive Strategy [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 471-479. |
[3] | CAO Bingquan1,2,3 (曹炳全), HE Yuesheng1,2,3∗ (贺越生), ZHUANG Hanyang4 (庄瀚洋), YANG Ming1,2,3 (杨 明). Infrastructure-Based Vehicle Localization System for Indoor Parking Lot Using RGB-D Cameras [J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 61-69. |
[4] | MAO Tianyang (茅天阳), ZHAO Wentao (赵文韬), WANG Jingchuan∗ (王景川), CHEN Weidong (陈卫东). Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration [J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 70-76. |
[5] | LÜ Qibing (吕其兵), LIU Tianyuan (刘天元), ZHANG Rong (张荣), JIANG Yanan (江亚南), XIAO Lei (肖雷), BAO Jingsong∗ (鲍劲松). Generation Approach of Human-Robot Cooperative Assembly Strategy Based on Transfer Learning [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 602-613. |
[6] | LIU Dasheng∗ (刘大生), YAN Guozheng (颜国正). Biomechanical Analysis of a Radial Expansion Mechanism of Intestinal Robot Coupling with Hyperelastic Intestinal Wall [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 552-560. |
[7] | LI Yanbiao∗ (李研彪), CHEN Ke (陈 科), SUN Peng (孙 鹏), WANG Zesheng (王泽胜). Dynamic Modeling and Performance Evaluation of a Novel Humanoid Ankle Joint [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 570-578. |
Viewed | ||||||||||||||||||||||||||||||||||||||||||||||||||
Full text 348
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||
Abstract 928
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||